Object classification and semantic segmentation of 3D models are foundations of numerous computer vision applications like autonomous driving and robot manipulation.
Thus far, a considerable number of convolutional neural networks (CNNs) have been developed for such tasks [1]}, [2]}, [3]}, [4]}, [5]}, [6]} and in most cases they yield promising results, especially when the distributions of test and train datasets are similar.
However, 3D models in the real world contain out-of-distribution samples, different samplings, noise and distortions that significantly influence their performance. Fig. REF  shows a few examples of wrong classification in the presence of noise.
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